System and method for linear distortion estimation by way of equalizer coefficients

ABSTRACT

Provided is a method and system for estimating distortion in a communications channel including an adaptive equalizer. The method includes determining one or more adaptive filter coefficients associated with a signal passed through the equalizer. The method also includes estimating un-equalized channel distortion based upon the determined adaptive filter coefficients.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of U.S. Provisional Application No.60/602,039 filed Aug. 17, 2004, which is incorporated herein byreference.

BACKGROUND OF THE INVENTION

1. Field of the Invention

The present invention relates to estimating distortion levels in achannel of a communications system.

2. Related Art

Conventional communications systems, such as a Data Over Cable ServiceInterface Specification (DOCSIS) based upstream systems, often receiveburst communications. To provide channel fidelity, such as adequatesignal to noise ratio (SNR) and signal power to distortion power ratio,many of these DOCSIS based systems (e.g., receivers) provide channelequalization. For short bursts that occur within suboptimal receivers,equalized receiver channels may provide little, if any, improvement overun-equalized channels. Thus, channel fidelity of short bursts,especially in the case of these suboptimal receivers, will beproblematic.

During short bursts, the practicality of using equalizer techniques suchas adaptive equalization, may be limited because the bursts may be tooshort to trigger or retain equalization benefits provided within thechannel. For example, many users may be contending for availablechannels within the system, with each channel employing unique userdependent equalization techniques.

The unique equalization technique employed is typically predicated uponreceipt and analysis of user data that is at least of minimum duration.A short communications burst, however, may not meet this minimumduration criteria. Therefore, although these short bursts may occur inan equalized channel, they can occur without the benefit of equalizationwhen their duration is shorter than the required minimum. One method inthe current art for overcoming this problem is for the subscriber tosend periodic training or ranging bursts, which contain largely knownsymbol patterns or training sequences. The receiver uses these trainingsequences to estimate the channel response and spectrum of anyinterference and noise on the channel. The receiver then downloadsequalizer coefficients to a pre-equalizer in the subscriber transmitter.This method of pre-equalization benefits both short and long packetssent by the subscriber.

Before the challenge of ensuring adequate channel fidelity can beremedied, it must be quantified. That is, before system designers andengineers can provide adequate channel fidelity for all users, theyshould especially understand the extent to which short burstcommunications can become degraded, or distorted. In addition, there maybe historical or economic reasons why equalization is not practicable ina given communications system. For example, the installed base of legacymodems may not support pre-equalization.

One traditional technique for understanding channel distortionassociated with short bursts, involves estimating a performance metric,such as the signal power to distortion power ratio. More specifically,this traditional technique includes estimating the signal power todistortion power ratio in a communications channel devoid ofequalization. Although this traditional technique can be implementedusing several different approaches, none of the resulting estimationsare particularly reliable.

One other traditional approach to estimating channel fidelity includesdesigning more complex receivers. More complex receivers, however, areless desirable because of factors such as cost, speed, and powerconsumption. Also, since many communication systems include thousands ofchannels. So more complex hardware and complex approaches that require,for example, special data development techniques, are undesirable.

What is needed, therefore, is a system and method for estimating thefidelity, or SNR, of a communications channel used without equalization.

SUMMARY OF INVENTION

Consistent with the principles of the present invention as embodied andbroadly described herein, the present invention includes a method forestimating distortion in a communications channel including an adaptiveequalizer. The method includes determining one or more adaptive filtercoefficients associated with a signal passed through the equalizer. Themethod also includes estimating un-equalized channel distortion basedupon the determined adaptive filter coefficients. It is assumed that thechannel(s) has little or no narrowband interference or “ingress” (i.e.,it contains predominantly white noise) and has a very high SNR(e.g., >30 dB) with respect to the background white noise.

The present invention provides a unique technique for estimating channelfidelity given the tap coefficients of an adaptive equalizer used withina communications channel within, for example, a set top cable box or acable modem.

In one embodiment of the present invention, a channel is first tested orsounded using a training or ranging transmission. Next, receiveequalization coefficients are developed based upon these tests. Thesecoefficients can be analyzed to determine whether the fidelity of thechannel will support the desired communications without the need ofreceiver equalization.

By knowing the fidelity of the communications channel withoutequalization, communications bursts can be formatted such that thebursts will successfully cross the channel without equalization.Especially for very short bursts (e.g., only a few bytes ofinformation), a lower order modulation (such as QPSK) can be employed.This lower order modulation should have a sufficient preamble andinclude forward error correction to provide reliable communicationsacross the channel without pre-equalization and with little, if any,receive equalization.

Pre-equalization can also be implemented, if the system supports it, andsubsequent residual receive equalizer coefficients can be developed. Thecoefficients are then analyzed to assess the fidelity (e.g., channellinear distortion) provided in light of pre-equalization.

Receiver equalization techniques can also be implemented. Since rangingand other overhead types of transmissions are often afforded incommunications systems for user channels, it is often the case thatreceiver equalization coefficients are readily available. For systemsthat support generation of pre-equalization coefficients, this data canbe readily generated and provided. Thus, it is desirable that thepresent inventive technique not be overly burdensome and complex, but beaccurate and reliable for estimating the linear distortion of thechannel.

In another embodiment of the present invention, a difference filter isdetermined between an all-pass equalizer (i.e., perfect impulseresponse) and a feed forward equalizer. The feed forward equalizer isdeveloped in relation to one or more of the communication channels. Thisapproach is referred to as the time domain approach and applies equallyfor a receiver equalizer or for a calculated transmitter pre-equalizer.

In this time domain approach, the power is determined in the differencefilter, (e.g., channel linear distortion filter) that is being equalizedby the channel. This computationally efficient method is referred to asthe time domain technique because it determines the difference filterpower via integration of the impulse response of the difference filter,in the time domain.

In yet another embodiment of the present invention, an estimate of thechannel response is determined by inverting the adaptive equalizerfiltering, after phase slope compensation. This approach is known as thefrequency domain approach. The distortion from this estimate of thechannel response is determined and compared to an all-pass filter. Again adjustment is made on the channel estimate frequency response(generated by inverting the frequency response of the equalizer,basically) to provide proper normalization.

The time domain approach, summarized above, does not take the inverse.However, as long as a determined channel frequency response is notsubstantially deviated from unity, failing to take the inverse may onlyintroduce modest inaccuracy. The time domain approach, for example,basically approximates 1/(1−epsilon) with 1+epsilon.

Further features and advantages of the present invention as well as thestructure and operation of various embodiments of the present invention,are described in detail below with reference to the accompanyingdrawings.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings, which are incorporated herein and constitutepart of the specification, illustrate embodiments of the presentinvention and, together with the general description given above and thedetailed description of the embodiments given below, serve to explainthe principles of the invention. In the drawings:

FIG. 1 is a block diagram illustration of a basic test system thatembodies the present invention;

FIG. 2 is a block diagram illustration of a distortion estimator used inthe test system of FIG. 1, constructed in accordance with one embodimentof the present invention;

FIG. 3 is a block diagram illustration of the distortion estimator usedin the test system of FIG. 1, constructed in accordance with anothersecond embodiment of the present invention;

FIG. 4 is a flow diagram of a conventional technique for estimatinglinear distortion in accordance with yet another embodiment of thepresent invention;

FIG. 5 is a flow diagram illustration of a method for estimating lineardistortion via adaptive equalizer taps in accordance with yet anotherembodiment of the present invention;

FIG. 6 is a flow diagram illustration of a basic method for estimatinglinear distortion via adaptive equalizer taps in accordance with anadditional embodiment of the present invention; and

FIG. 7 is a block diagram of an exemplary computer system on which thepresent invention can be practiced.

DETAILED DESCRIPTION OF INVENTION

The following detailed description of the present invention refers tothe accompanying drawings that illustrate exemplary embodimentsconsistent with this invention. Other embodiments are possible, andmodifications may be made to the embodiments within the spirit and scopeof the invention. Therefore, the following detailed description is notmeant to limit the invention. Rather, the scope of the invention isdefined by the appended claims.

It would be apparent to one skilled in the art that the presentinvention, as described below, may be implemented in many differentembodiments of hardware, software, firmware, and/or the entitiesillustrated in the drawings. Any actual software code with thespecialized controlled hardware to implement the present invention isnot limiting of the present invention. Thus, the operation and behaviorof the present invention will be described with the understanding thatmodifications and variations of the embodiments are possible, given thelevel of detail presented herein.

FIG. 1 is a block diagram illustration of a basic test system tool 100in which the present invention can be implemented. In the system 100 ofFIG. 1, an input signal is received within a communications channel 102to produce a channel output signal 103. The channel output signal 103 isforwarded along an un-equalized communications path 104. Thecommunications channel 102 can include, for example, a conventionalcommunications receiver.

Next, the output signal 103 is forwarded to un-equalized slicer 106,where an initial symbol determination is made. A sliced signal, outputfrom the slicer, is forwarded to a combiner 108. At the same time, theun-equalized output signal 103 is also forwarded along an alternate path105 to the combiner 108, where it is combined with the sliced signal toform an error signal 107. Next, a SNR measurement device 110 measuresthe SNR of the error signal 107. The SNR measurement device 110 producesan actual SNR measurement value 112, which can then be used as a benchmetric by a system tester.

The output signal 103 is also forwarded along a parallel communicationspath 114. The parallel path 114, however, provides equalization. Forexample, along the path 114, the output signal 103 is provided as aninput to an adaptive equalizer 116. The adaptive equalizer 116determines equalization coefficients associated with the output signal103, as forwarded through the communications channel 102. The adaptiveequalizer 116 produces equalizer coefficients 117 as an output. Theequalizer coefficients 117 are provided as an input to an SNR estimator118.

The purpose of the SNR estimator 118 is to estimate what the SNR of theslicer 106 would have been if the equalizer were not present. Thisestimation is accomplished within the SNR estimator 118 by analyzing theequalizer coefficients 117. The distortion in the channel 102 can thenbe inferred from the coefficients 117, and the error power resultingfrom that distortion can be estimated, resulting in an estimated SNR120.

Next, the measured SNR 112 and the estimated SNR 120 are compared withinan SNR comparison device 122 to determine how well the SNR estimator 118succeeded in predicting the SNR of the un-equalized output of thechannel 102 along the communications path 104. During testing, it hasbeen noted that the measured SNR 112 and the estimated SNR 120 havematched within about 1 dB in typical upstream DOCSIS channels.

FIG. 2 is a block diagram illustration of the SNR estimator 118 of FIG.1, based upon one implementation of the time domain approach. In FIG. 2,the equalizer coefficients 117, expressed here as h1(n), are created bytraining the adaptive equalizer 116 on the output signal 103, passedthrough the channel 102 of FIG. 1. The coefficients h1(n) are receivedas inputs to an energy normalization unit 200, where the coefficientsare normalized to unit energy, producing normalized coefficients 201.

The normalized coefficients 201 are then differenced with an idealchannel response (i.e., delayed unit impulse) h0(n) within a combiner202. The combiner 202 produces a difference sequence h2(n) as an output.The delayed unit impulse is defined as a value of 1 in the same positionof the main tap of the equalizer coefficients h1(n), with all otherelements of the unit impulse sequence being zeros. Additionally, thedelayed unit impulse has the same overall length as the equalizercoefficient sequence h1(n).

The difference sequence h2(n) is also known in the art as an errorfilter response. If the channel 102 was an ideal channel, the equalizertaps h1(n) would also be an impulse response, and the differencesequence h2(n) would be all zeros. Energy within the error filter, whichis a measure of distortion, is determined within an energy module 204.The error filter energy is measured to produce the SNR estimate 120,shown in FIG. 1.

FIG. 3 is a block diagram illustration 300 of the SNR estimator 118 ofFIG. 1, based upon one implementation of the frequency domain approach.In FIG. 3, the equalizer coefficients h1(n), that resulted from trainingthe adaptive equalizer 116, are again received as an input to the energynormalization unit 200, where the error sequence h2(n) is determined. Inthe frequency domain approach of FIG. 3, a fast Fourier transform (FFT)of h2(n) is taken within an FFT module 302, yielding H2(k). The termH2(k) is the frequency response of h2(n). Note that the term (k) is afrequency variable and is omitted in some cases, within the context ofFIG. 3, for simplification. Within the FFT module 302, h2(n) is firstpadded with zeros in order to smooth the frequency response H2(k). TheFFT (e.g., preferably of length N (typically 4096) samples) is thentaken, giving the frequency-domain sequence H2(k).

Next, the delay offset of the frequency response H2(k) is adjustedwithin a delay adjustment module 304. A delay offset adjustment isperformed since the equalizer coefficients h1(n) have their main tap ina position other than the first tap. For example, the main tap istypically the 4th tap position in an 8-tap equalizer, while the idealchannel frequency response (i.e., unity, or 1, or a flat frequencyresponse) corresponds to a unit impulse having its unity tap in thefirst tap location.

More specifically, the delay adjustment, within the delay adjustmentmodule 304, includes removing a phase response corresponding to thedelay of the main tap. A delay-adjusted error frequency response H3(k)is output from the delay adjustment module 304.

The error response H3(k) is then added to the ideal channel frequencyresponse (unity, or 1, or flat frequency response), and inverted withinan inverting module 306. The inverting module 306 produces an estimatedchannel response H4(k) as an output. The inverting module 306 providesthe estimate of the channel response H4(k) as representative of theadaptive equalizer 116 adjusting its coefficients to approximatelyinvert the channel.

The estimated channel response H4(k) is normalized to unit power withina power normalization unit 308, producing a normalized signal H5(k). Anamount of phase offset is removed from the normalized signal H5(k)within an offset removal unit 310. The offset removal unit 310 producesan output of H6(k), which is the final estimate of the channel response.

The estimated channel response H6(k) is then subtracted from the idealchannel frequency response (unity, or 1, or flat frequency response)within a combiner 311. The combiner 311 produces as an output, an errorfilter response H7(k), which represents a measure of the distortion.Error energy is determined within an error power module 312. An outputof the error power module 312 is a slightly more accurate reading of theSNR estimate 120, than the estimate produced by the time domain approachof FIG. 2.

FIG. 4 is a flow diagram of a technique 400 for estimating lineardistortion in accordance with another embodiment of the presentinvention. In FIG. 4, a communications burst is transmitted, forexample, from a communications transmitter, as indicated in step 402.Next, the transmitted burst is received, for example, within a receiverequalizer and feed forward equalizer taps are adapted, as indicated instep 404. In step 406, power is summed in all the tap coefficients,except the main tap. This sum is then divided by the total tap power toproduce a distortion power to signal power ratio metric 408. This signalpower to distortion power ratio is simply one metric by which anestimate of the distortion, created on a digital communications channel,can be measured.

FIG. 5 is a flow diagram illustration of a method 500 for estimatinglinear distortion via adaptive equalizer taps, based upon anotherimplementation of the time domain approach. In the method 500, steps402-404 are performed, as illustrated in FIG. 4. Next, a gain correctionis performed such that when the power in all the adaptive equalizer tapsare summed, the result is the normalized unity power, as indicated instep 502.

In step 504, a difference filter is determined. The difference filter isthe difference of an all-pass filter, with the same power as thenormalized equalizer filter, and the same delay as the equalizer has toits main tap, or decision tap. In other words, in the case of unitypower normalization, the all-pass filter has value of 1 in the main taplocation of the equalizer filter and a value of zero for thecoefficients of all the other tap locations. The power in thisdifference filter can be determined in the time domain. The main tap,however, is the difference between unity and the equalizer main tapcoefficient.

The time domain implementation of FIG. 5 in essence approximates theinverse equalizer filter by using the equalizer filter amplituderesponse as a substitute. To examine how this might work as anapproximation, recall that for absolute value of “epsilon” very smallcompared to unity,1/(1+epsilon)≈1−epsilon.  [Equation 1]

Thus,“adjusted inverse equalizer filter”=all pass filter plus residualdistortion,  [Equation 2]=1+epsilon.  [Equation 3]

Typically,“equalizer filter”≈1/“adjusted inverse equalizer filter”  [Equation 4]=1/(1+epsilon)  [Equation 5]

Thus,“equalizer filter”≈1/(1+epsilon)≈1−epsilon.  [Equation 6]

And note that in the Time Domain technique the “difference filter” isdetermined such that:“equalizer filter”=all-pass filter plus “difference filter.”  [Equation7]

Thus, comparing the expressions for the “equalizer filter” in Equations6 and 7, it can be seen that the frequency response of the “differencefilter” is well-approximated as“difference filter”≈−epsilon,

as long as the residual distortion filtering is small at all frequencysamples.

The residual distortion in the frequency domain approaches, generally,is represented by epsilon [actually, a different value at each frequencybin, such as epsilon(f)]. The frequency response of the differencefilter in the time domain technique is approximated as (−epsilon).Although there is a negative sign which is not shared, this isirrelevant when the distortion power is calculated in the techniques ofthe present invention. (Note that in the time domain technique, thefrequency response is not actually determined. The distortion power isdetermined in the time domain.)

Thus, if the residual distortion in the adjusted inverse equalizerchannel, represented at each frequency sample by epsilon in the aboveapproximation, is very small in amplitude, then it is well approximatedby the difference filter determined from the taps in the time domaintechnique. This is true except for the negative sign, which, whencalculating power (as in the distortion power), becomes insignificant.

FIG. 6 is a flow diagram illustration of a method 600 for estimatinglinear distortion via adaptive equalizer taps, based upon anotherimplementation of the frequency domain approach. In FIG. 6, the method600 performs steps 402-404, shown in FIG. 4 and step 502 shown in FIG.5. The method 600 also includes step 504 of FIG. 5, which determines thedifference filter.

Next, step 602 of FIG. 6 determines the frequency response of thedifference filter. The frequency response is determined by taking theFFT of the difference filter, converting the difference filter from timedomain to frequency domain. In step 604, a phase slope corresponding tothe delay to the decision tap (i.e., main tap) of the adaptive equalizeris compensated or removed from the frequency response, determined instep 602. This is the true frequency response of the residual filteringapplied by the equalizer. In step 606, this residual equalizer frequencyresponse is added to the all-pass, zero-delay, flat phase, gainnormalized filter.

The residual equalizer filter serves as the ideal reference (normalizedto the same power as used for normalizing the equalizer in the timedomain), yielding the delay-compensated (i.e., zero-delay) equalizerfrequency response. Note that this composite filter can be determined bydelay compensating the frequency response of the equalizer.

In step 608, one frequency sample at a time, the zero-delay equalizerfrequency response is inverted. That is, the multiplicative complexinverse is determined for each sample. The result is the inverseequalizer filter, which is the multiplicative inverse of the equalizer.When the multiplicative inverse is concatenated with the equalizer, theflat response (i.e., zero phase response with only delay), is provided.

In a zero-forcing equalizer, or a least mean square (LMS) equalizer withno noise and perfect convergence of the equalizer tap coefficients, thisinverse equalizer filter is the channel frequency response for which theequalizer would perfectly compensate.

Adapting with a zero-forcing technique (i.e., LMS technique with nonoise), the equalizer itself is the best equalizer (best in amean-squared error fit) of its length (number of taps), and main taplocation, that can be obtained. Note that with a T-spaced equalizer, andwith symbol shaping modulation, the mean-squared error best fit, justreferenced, actually becomes a weighted fit in the frequency domain.This is not an equal weighting of the distortion or error at eachfrequency sample.

This inverse equalizer filter represents the channel frequency responsewhich the equalizer is perfectly compensating. The distortion power inthis filter represents the best approximation of the linear distortionwhich can be gleaned from the equalizer taps (assuming nonlineardistortion and noise are negligible during the equalizer convergence).

The inverse equalizer filter provides good information about the channeldistortions the equalizer is combating. However, it is a less optimalmethod for determining the linear distortion the channel imposes upondigital modulation. Receivers of digital modulation transmissions mustdevelop carrier tracking, including carrier phase estimation andcompensation. Similarly, for modulation schemes such as 16QAM (andhigher density), good gain adjustments should be developed for thereceivers to distinguish higher amplitude symbols from lower amplitudesymbols during the transmissions.

As such, any final manipulations of the inverse equalizer filtercomprise a gain adjustment, normalizing the power of this filter (e.g.,to unity), as indicated in step 610. In step 612, an average phase isdetermined and the phase is de-rotated. This brings the average phase ofthe frequency response of the inverse equalizer filter, to zero degrees.By way of example, there are several methods to consider for determiningthe average phase.

One exemplary method for determining the average phase of step 612performs a vector addition of the complex values of all the frequencysamples in the inverse equalizer filter. This particular method alsouses the phase of the resultant complex vector as the phase of the(i.e., estimated) channel which is to be zeroed out (i.e., rotated out).

Another technique for determining the average phase, for example, caninclude weighting the frequency response complex samples with the powerspectral density of the digital modulation.

Finally, the gain and phase compensated inverse equalizer filter isprocessed to generate the estimated distortion due to the channel linearfiltering, which is imposed upon digital modulation. In step 614, theall-pass filter is subtracted from the adjusted inverse equalizerfilter, frequency-sample-by-frequency-sample, to create the frequencyresponse of the estimated residual distortion.

In step 616, the power in this residual distortion is totaled andcompared with the power in the all-pass filter (all-pass filterpower/residual distortion power). This total produces the estimateddistortion-to-signal power ratio of the estimated linear filtering ofthe channel, derived from adaptive equalizer taps adapted via use of thechannel.

FIG. 7 is a block diagram of an exemplary computer system on which thepresent invention can be practiced. The following description of theexemplary computer system is provided for completeness. The presentinvention can be implemented in hardware, or as a combination ofsoftware and hardware. Consequently, the invention may be implemented inthe environment of a computer system or other processing system.

In the present invention, all of the elements depicted in FIGS. 1-6, forexample, can execute on one or more distinct computer systems 700, toimplement the various methods of the present invention. The computersystem 700 includes one or more processors, such as a processor 704. Theprocessor 704 can be a special purpose or a general purpose digitalsignal processor.

The processor 704 is connected to a communication infrastructure 706(for example, a bus or network). Various software implementations aredescribed in terms of this exemplary computer system. After reading thisdescription, it will become apparent to a person skilled in the relevantart how to implement the invention using other computer systems and/orcomputer architectures.

The computer system 700 also includes a main memory 708, preferablyrandom access memory (RAM), and may also include a secondary memory 710.The secondary memory 710 may include, for example, a hard disk drive 712and/or a removable storage drive 714, representing a floppy disk drive,a magnetic tape drive, an optical disk drive, etc.

The removable storage drive 714 reads from and/or writes to a removablestorage unit 718 in a well known manner. The removable storage unit 718,represents a floppy disk, magnetic tape, optical disk, etc. which isread by and written to by the removable storage drive 714. As will beappreciated, the removable storage unit 718 includes a computer usablestorage medium having stored therein computer software and/or data.

In alternative implementations, the secondary memory 710 may includeother similar means for allowing computer programs or other instructionsto be loaded into the computer system 700. Such means may include, forexample, a removable storage unit 722 and an interface 720.

Examples of such means may include a program cartridge and cartridgeinterface (such as that found in video game devices), a removable memorychip (such as an EPROM, or PROM) and associated socket, and otherremovable storage units 722 and interfaces 720 which allow software anddata to be transferred from the removable storage unit 722 to thecomputer system 700.

The computer system 700 may also include a communications interface 724.The communications interface 724 allows software and data to betransferred between the computer system 700 and external devices.Examples of the communications interface 724 may include a modem, anetwork interface (such as an Ethernet card), a communications port, aPCMCIA slot and card, etc.

Software and data transferred via the communications interface 724 arein the form of signals which may be electronic, electromagnetic, opticalor other signals capable of being received by the communicationsinterface 724. These signals are provided to the communicationsinterface 724 via a communications path. The communications path carriessignals and may be implemented using wire or cable, fiber optics, aphone line, a cellular phone link, an RF link and other communicationschannels.

In this document, the terms computer program medium and computerreadable medium are used to generally refer to media such as theremovable storage drive 714, a hard disk installed in hard disk drive712, and the signals. These computer program products are means forproviding software to the computer system 700.

Computer programs (also called computer control logic) are stored in themain memory 708 and/or the secondary memory 710. Computer programs mayalso be received via the communications interface 724. Such computerprograms, when executed, enable the computer system 700 to implement thepresent invention as discussed herein. In particular, the computerprograms, when executed, enable the processor 704 to implement theprocesses of the present invention. Accordingly, such computer programsrepresent controllers of the computer system 700.

By way of example, in the embodiments of the invention, theprocesses/methods performed by signal processing blocks of encodersand/or decoders can be performed by computer control logic. Where theinvention is implemented using software, the software may be stored in acomputer program product and loaded into the computer system 700 usingthe removable storage drive 714, the hard drive 712 or thecommunications interface 724.

CONCLUSION

The present invention provides a unique technique for estimating channelfidelity given the tap coefficients of an adaptive equalizer used withina communications channel within, for example, a set top cable box or acable modem.

One advantage of the present invention is that it provides a goodestimate of the distortion imposed by a channel upon digital modulation.This is especially true when adaptive equalization is not used, eitherby choice, or when equalization is not capable of being fully converged(such as with short transmission bursts, without pre-equalization).Additionally, the present invention uses commonly available data in acommunications system since equalizer coefficients are commonlydeveloped on the long ranging burst(s) required for initial registrationof a user into a system, such as with DOCSIS.

The frequency domain approach, noted above, primarily requires, forexample, an FFT-based technique (starting with only small number of timedomain samples) and phase compensation (complex multiplies) across thefrequency samples. The FFT technique also includes addition across thefrequency samples (of the all-pass filtering), complex multiplicativeinversion across the frequency samples, several normalizations (gain andphase), and subtraction across the frequency domain (of the all-passfilter). Finally, The FFT technique performs a power computation acrossthe frequency domain.

The present invention has been described above with the aid offunctional building blocks illustrating the performance of specifiedfunctions and relationships thereof. The boundaries of these functionalbuilding blocks have been arbitrarily defined herein for the convenienceof the description. Alternate boundaries can be defined so long as thespecified functions and relationships thereof are appropriatelyperformed.

Any such alternate boundaries are thus within the scope and spirit ofthe claimed invention. One skilled in the art will recognize that thesefunctional building blocks can be implemented by analog and/or digitalcircuits, discrete components, application-specific integrated circuits,firmware, processor executing appropriate software, and the like, or anycombination thereof. Thus, the breadth and scope of the presentinvention should not be limited by any of the above-described exemplaryembodiments, but should be defined only in accordance with the followingclaims and their equivalents.

The foregoing description of the specific embodiments will so fullyreveal the general nature of the invention that others can, by applyingknowledge within the skill of the art (including the contents of thereferences cited herein), readily modify and/or adapt for variousapplications such specific embodiments, without undue experimentation,without departing from the general concept of the present invention.Therefore, such adaptations and modifications are intended to be withinthe meaning and range of equivalents of the disclosed embodiments, basedon the teaching and guidance presented herein.

It is to be understood that the phraseology or terminology herein is forthe purpose of description and not of limitation, such that theterminology or phraseology of the present specification is to beinterpreted by the skilled artisan in light of the teachings andguidance presented herein, in combination with the knowledge of one ofordinary skill in the art.

The Detailed Description section should primarily be used to interpretthe claims. The Summary and Abstract sections may set forth one or more,but not all exemplary embodiments of the present invention ascontemplated by the inventor(s), and thus, are not intended to limit theclaims.

1. A method for estimating channel distortion in a communications systemchannel including an adaptive equalizer, comprising: determining one ormore adaptive filter coefficients associated with a signal by theadaptive equalizer; normalizing the determined one or more adaptivefilter coefficients to unity power; differencing the normalized one ormore adaptive filter coefficients with an all-pass filter, thedifferencing including a difference between an equalizer main tapcoefficient and unity; and estimating the channel distortion based uponthe differencing of the normalized one or more adaptive filtercoefficients including the equalizer main tap coefficient.
 2. The methodof claim 1, wherein the channel distortion includes at least one oflinear filtering distortion, signal power to distortion power ratio,modulation error ratio, and signal to noise ratio.
 3. The method ofclaim 2, wherein the communications system channel is a transmitchannel.
 4. The method of claim 2, wherein the communications systemchannel is a receive channel.
 5. The method of claim 1, furthercomprising calculating a frequency response of the determined one ormore adaptive filter coefficients; and applying corrections to thefrequency response, wherein the applying corrections step occurs priorto estimating the channel distortion.
 6. The method of claim 5, whereinthe frequency response is determined in accordance with a fast Fouriertransform operation.
 7. The method of claim 5, wherein the correctionsinclude adjustments to amplitude and phase of the frequency response. 8.A method for estimating noise in a communications channel including anadaptive equalizer, comprising: determining one or more adaptive filtercoefficients h₁(n) associated with a signal by the adaptive equalizer;normalizing the determined one or more adaptive filter coefficients tounit energy; and differencing the normalized one or more adaptive filtercoefficients with an ideal response h₀(n) of the communications channel,the differencing producing an error sequence h₂(n).
 9. The method ofclaim 8, further comprising determining an energy of the error sequence.10. The method of claim 8, further comprising: converting the errorsequence to frequency domain, the converting producing a frequencyresponse H₂(k) of the error sequence; and removing a delay offset of thefrequency response to produce a delayed offset response H₃(k).
 11. Themethod of claim 10, further comprising: (i) summing the delayed offsetresponse and an ideal channel frequency response and (ii) inverting thesum to produce an inverted response H₄(k); normalizing the invertedresponse to unit power, thus producing a normalized response H₅(k); andremoving a phase offset from the normalized inverted response, theremoving producing an estimated channel response H₆(k).
 12. The methodof claim 11, further comprising: differencing the estimated channelresponse H₆(k) with the ideal channel frequency response, thedifferencing producing an error filter response H₇(k); and determiningan error power of the difference.
 13. The method of claim 10, whereinthe converting is based upon a fast Fourier transform of the errorsequence.
 14. The method of claim 10, wherein the converting is basedupon (i) padding the error sequence with zeros and (ii) applying a fastFourier transform (FFT) to the padded error sequence.
 15. The method ofclaim 14, wherein the FFT includes at least 4096 samples.
 16. A methodfor estimating noise in a communications channel including an adaptiveequalizer, comprising: receiving data within the communications channeland converging taps of the adaptive equalizer when the data is received;determining a sum of tap power values, the sum including a power of amain tap and dividing the sum by a combined power of all the taps;normalizing the taps to unity power; determining a difference filterfrom the normalized taps; and summing a power of taps of the differencefilter.
 17. An apparatus for estimating channel distortion in acommunications system channel including an adaptive equalizer,comprising: means for determining one or more adaptive filtercoefficients associated with a signal by the adaptive equalizer; meansfor normalizing the determined one or more adaptive filter coefficientsto unity power; means for differencing the normalized one or moreadaptive filter coefficients with an all-pass filter, the differencingincluding a difference between an equalizer main tap coefficient andunity; and means for estimating the channel distortion based upon thedifferencing of the normalized one or more adaptive filter coefficientsincluding the equalizer main tap coefficient.
 18. The apparatus of claim17, wherein the channel distortion includes at least one of linearfiltering distortion, signal power to distortion power ratio, modulationerror ratio, and signal to noise ratio.
 19. The apparatus of claim 18,further comprising means for calculating a frequency response of thedetermined one or more adaptive filter coefficients; and means forapplying corrections to the frequency response.
 20. The apparatus ofclaim 19, wherein the frequency response is determined in accordancewith a fast Fourier transform operation.
 21. The apparatus of claim 20,wherein the corrections include adjustments to amplitude and phase ofthe frequency response.
 22. An apparatus for estimating noise in acommunications channel including an adaptive equalizer, comprising:means for determining one or more adaptive filter coefficients h₁(n)associated with a signal by the adaptive equalizer; means fornormalizing the determined one or more adaptive filter coefficients tounit energy; and means for differencing the normalized one or moreadaptive filter coefficients with an ideal response h₀(n) of thecommunications channel, the differencing producing an error sequenceh₂(n).
 23. The apparatus of claim 22, further comprising means fordetermining an energy of the error sequence.
 24. The apparatus of claim23, further comprising: means for converting the error sequence tofrequency domain, the converting producing a frequency response H₂(k) ofthe error sequence; and means for removing a delay offset of thefrequency response to produce a delayed offset response H₃(k).
 25. Theapparatus of claim 24, further comprising: means for (i) summing thedelayed offset response and an ideal channel frequency response and (ii)inverting the sum to produce an inverted response H₄(k); means fornormalizing the inverted response to unit power, thus producing anormalized response H₅(k); and means for removing a phase offset fromthe normalized inverted response, the removing producing an estimatedchannel response H₆(k).
 26. The apparatus of claim 25, furthercomprising: means for differencing the estimated channel response H₆(k)with the ideal channel frequency response, the differencing producing anerror filter response H₇(k); and means for determining an error power ofthe difference.
 27. A computer readable medium carrying one or moresequences of one or more instructions for execution by one or moreprocessors to perform a method for estimating channel distortion in acommunications system channel including an adaptive equalizer, theinstructions when executed by the one or more processors, cause the oneor more processors to perform the steps of: determining one or moreadaptive filter coefficients associated with a signal by the adaptiveequalizer; normalizing the determined one or more adaptive filtercoefficients to unity power; differencing the normalized one or moreadaptive filter coefficients with an all-pass filter, the differencingincluding a difference between a normalized equalizer main tapcoefficient and unity; and estimating un-equalized channel distortionbased upon the differencing of the normalized one or more adaptivefilter coefficients including the equalizer main tap coefficient.